The subject technology relates generally to exploring data stored in data sources and, more particularly, to a system and method that allows the use of native spreadsheet functionality in a spreadsheet application to be used to explore external data sources through translation of spreadsheet formulas into standard database language (i.e. SQL) for processing on the external data sources.
Accessing large data engines today requires knowledge of the functionality and language of the particular data engines. There are many types of data engines, with new ones being released constantly. Each type of data engine has either slight differences in implementation and extensions to standards, or wholly new approaches such as the recent arrival of map-reduce based data stores. This presents two problems for end users. First, the end users must have specific knowledge and skill concerning the data engine in use which requires a learning curve or prior training. Second, most organizations have many different data engines storing collections of available data. This use of different data engines across an organization requires the end user to have specific knowledge of each data engine plus the comparisons of functionality across them to enable cross engine usage.